

from __future__ import annotations

from decimal import Decimal

from numpy import array


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(a__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
						# Calculate the determinant of the matrix
						a_						:						Optional[int] 							= float(
						    d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
						if determinant == 0:
									raise ValueError("""This matrix has no inverse.""")

						# Creates a copy of the matrix with swapped positions of the elements
						a_						:						List[Any] 							= [[0.0, 0.0], [0.0, 0.0]]
						a_							,					a_						:						Optional[Any] 							= matrix[1][1], matrix[0][0]
						a_							,					a_						:						Optional[int] 							= -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(a__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(a__) == 3
			    and len(matrix[0]) == 3
			    and len(matrix[1]) == 3
			    and len(matrix[2]) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						a_						:						List[str] 							= float(
						    (
						        (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
						        + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
						        + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
						    )
						    - (
						        (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
						        + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
						        + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
						    ))
						if determinant == 0:
									raise ValueError("""This matrix has no inverse.""")

						# Creating cofactor matrix
						a_						:						List[Any] 							= [
						    [d(0.0), d(0.0), d(0.0)],
						    [d(0.0), d(0.0), d(0.0)],
						    [d(0.0), d(0.0), d(0.0)],
						]
						a_						:						str 							= (d(matrix[1][1]) * d(matrix[2][2])) - (
						    d(matrix[1][2]) * d(matrix[2][1])
						)
						a_						:						Dict 							= -(
						    (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
						)
						a_						:						Dict 							= (d(matrix[1][0]) * d(matrix[2][1])) - (
						    d(matrix[1][1]) * d(matrix[2][0])
						)
						a_						:						str 							= -(
						    (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
						)
						a_						:						List[Any] 							= (d(matrix[0][0]) * d(matrix[2][2])) - (
						    d(matrix[0][2]) * d(matrix[2][0])
						)
						a_						:						Any 							= -(
						    (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
						)
						a_						:						Tuple 							= (d(matrix[0][1]) * d(matrix[1][2])) - (
						    d(matrix[0][2]) * d(matrix[1][1])
						)
						a_						:						int 							= -(
						    (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
						)
						a_						:						str 							= (d(matrix[0][0]) * d(matrix[1][1])) - (
						    d(matrix[0][1]) * d(matrix[1][0])
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						a_						:						Tuple 							= array(a__)
						for i in range(3):
									for j in range(3):
												a_						:						Dict 							= cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						a_						:						Dict 							= array(a__)
						for i in range(3):
									for j in range(3):
												inverse_matrix[i][j] /= d(a__)

        # Calculate the inverse of the matrix
						return [[float(d(a__)) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")





import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__snake_case				:      Optional[int]   		=			logging.getLogger(__name__)



class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None    )     -> Tuple:
									a_						:						str 							= self.layer[current_layer](_lowercase  ,      _lowercase  ,      head_mask[current_layer]    )

									a_						:						str 							= layer_outputs[0]

									return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> List[str]:
									super().__init__(_lowercase    )

									a_						:						Tuple 							= BertEncoderWithPabee(_lowercase    )

									self.init_weights()
									a_						:						int 							= 0
									a_						:						Any 							= 0
									a_						:						Tuple 							= 0

									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Tuple:
									a_						:						Dict 							= threshold





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[Any]:
									a_						:						Optional[int] 							= patience





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						str 							= 0
									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= self.inference_layers_num / self.inference_instances_num
									a_						:						Optional[int] 							= (
									    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
									    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
									)
									print(_lowercase    )





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      )     -> str:

									if input_ids is not None and inputs_embeds is not None:
												raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""    )
									elif input_ids is not None:
												a_						:						Dict 							= input_ids.size()
									elif inputs_embeds is not None:
												a_						:						Dict 							= inputs_embeds.size()[:-1]
									else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

									a_						:						Optional[Any] 							= input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
												a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
									if token_type_ids is None:
												a_						:						List[str] 							= torch.zeros(_lowercase  ,      dtype=torch.long  ,      device=_lowercase    )

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									a_						:						torch.Tensor 							= self.get_extended_attention_mask(_lowercase  ,      _lowercase  ,      _lowercase    )

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
												a_							,					a_							,					a_						:						int 							= encoder_hidden_states.size()
												a_						:						List[str] 							= (encoder_batch_size, encoder_sequence_length)
												if encoder_attention_mask is None:
															a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
												a_						:						List[Any] 							= self.invert_attention_mask(_lowercase    )
									else:
												a_						:						Optional[Any] 							= None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									a_						:						List[Any] 							= self.get_head_mask(_lowercase  ,      self.config.num_hidden_layers    )

									a_						:						List[str] 							= self.embeddings(
									    input_ids=_lowercase  ,      position_ids=_lowercase  ,      token_type_ids=_lowercase  ,      inputs_embeds=_lowercase    )
									a_						:						List[Any] 							= embedding_output

									if self.training:
												a_						:						Any 							= []
												for i in range(self.config.num_hidden_layers    ):
															a_						:						int 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						List[Any] 							= self.pooler(_lowercase    )
															a_						:						Optional[int] 							= output_layers[i](output_dropout(_lowercase    )    )
															res.append(_lowercase    )
									elif self.patience == 0:  # Use all layers for inference
												a_						:						Union[str, Any] 							= self.encoder(
												    _lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
												a_						:						Union[str, Any] 							= self.pooler(encoder_outputs[0]    )
												a_						:						List[str] 							= [output_layers[self.config.num_hidden_layers - 1](_lowercase    )]
									else:
												a_						:						Any 							= 0
												a_						:						Dict 							= None
												a_						:						Tuple 							= 0
												for i in range(self.config.num_hidden_layers    ):
															calculated_layer_num += 1
															a_						:						Optional[Any] 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						Optional[int] 							= self.pooler(_lowercase    )
															a_						:						int 							= output_layers[i](_lowercase    )
															if regression:
																		a_						:						Dict 							= logits.detach()
																		if patient_result is not None:
																					a_						:						Optional[Any] 							= patient_result.detach()
																		if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
																					patient_counter += 1
																		else:
																					a_						:						Optional[Any] 							= 0
															else:
																		a_						:						str 							= logits.detach().argmax(dim=1    )
																		if patient_result is not None:
																					a_						:						str 							= patient_result.detach().argmax(dim=1    )
																		if (patient_result is not None) and torch.all(labels.eq(_lowercase    )    ):
																					patient_counter += 1
																		else:
																					a_						:						Tuple 							= 0

															a_						:						Union[str, Any] 							= logits
															if patient_counter == self.patience:
																		break
												a_						:						str 							= [patient_result]
												self.inference_layers_num += calculated_layer_num
												self.inference_instances_num += 1

									return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> str:
									super().__init__(_lowercase    )
									a_						:						str 							= config.num_labels

									a_						:						Optional[Any] 							= BertModelWithPabee(_lowercase    )
									a_						:						int 							= nn.Dropout(config.hidden_dropout_prob    )
									a_						:						str 							= nn.ModuleList(
									    [nn.Linear(config.hidden_size  ,      self.config.num_labels    ) for _ in range(config.num_hidden_layers    )]    )

									self.init_weights()





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Optional[Any] 							= self.bert(
									    input_ids=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      position_ids=_lowercase  ,      head_mask=_lowercase  ,      inputs_embeds=_lowercase  ,      output_dropout=self.dropout  ,      output_layers=self.classifiers  ,      regression=self.num_labels == 1  ,      )

									a_						:						Optional[Any] 							= (logits[-1],)

									if labels is not None:
												a_						:						int 							= None
												a_						:						Union[str, Any] 							= 0
												for ix, logits_item in enumerate(_lowercase    ):
															if self.num_labels == 1:
																		#  We are doing regression
																		a_						:						Any 							= MSELoss()
																		a_						:						Union[str, Any] 							= loss_fct(logits_item.view(-1    )  ,      labels.view(-1    )    )
															else:
																		a_						:						Any 							= CrossEntropyLoss()
																		a_						:						int 							= loss_fct(logits_item.view(-1  ,      self.num_labels    )  ,      labels.view(-1    )    )
															if total_loss is None:
																		a_						:						str 							= loss
															else:
																		total_loss += loss * (ix + 1)
															total_weights += ix + 1
												a_						:						Any 							= (total_loss / total_weights,) + outputs

									return outputs


